Uncertainty estimation of a liquid flow standard system with small flow rates

A liquid flow standard system is used to calibrate liquid volume of fuel–oil flow meters at small flow rates between 50 L/h and 700 L/h. However, the system has not been used to calibrate volume flow rate because the system is only operated with the standing-start-and-finish mode. In this study, the liquid flow standard system was rebuilt to provide a calibration service of volume flow rate by attaching two flow diverters, which can operate the system with the flying-start-and-finish mode. To evaluate its performance for volume flow metering, several techniques were introduced. First, diverter timing errors were estimated by linear regression. Second, covariance between buoyancy correction factor and water density was obtained to consider interdependency between the two measurands. Third, calibration and measurement capability (CMC) was evaluated by setting a fixed value of collected weight or elapsed time for flow diversion. Finally, several CMCs were compared to find the best measurement condition. As a result of the above approach, the CMC of the liquid flow standard system was found to be (0.10–0.52)% (k = 2) for (50–700) L/h with a minimum collected weight at 10 kg.     

Indexes for performance evaluation of cameras applied to dynamic measurements

Thanks to technology improvements, the applications of vision-based measurement to dynamic applications have been increasing in the last years. The available image resolutions and the high grabbing frequencies allow to acquire high-speed moving object with a good scaling factor and to perform dynamic analysis of vibrating items. Uncertainty analysis of vision-based measuring devices working in almost-static conditions was widely studied in literature, but the case of dynamic measurements still needs a further analysis. The measuring performances thus depend on the well-known parameters that affect the static performances (image resolution and contrast, processing algorithm, noise, etc.) but also on other factors, above all the exposure time and the camera-object relative motion, in terms of instantaneous velocity and acceleration. In this work, a performance analysis of imaging devices applied to dynamic measurements is proposed. The analysis aims to qualify the measurement uncertainty by some indexes, proposed in this work, and designed to quantify the motion effect on the acquired images and consequently the measurement uncertainty. These indexes are based on exposure time and Spatial Frequency Response (SFR) function, which is widely applied in literature and recommended in international standards for the image quality estimation in static acquiring conditions. Appropriate developments of SFR are proposed herein to obtain information on the image quality grabbed in dynamic conditions. The effectiveness of the proposed indexes are proved by several tests, where a target is moved with an harmonic law in controlled condition (varying its frequency and amplitude) and fixing different acquisition conditions in terms of lighting settings, diaphragm aperture, exposure time, etc.     

Generalized eigenvalue minimization for uncertain first-order plus time-delay processes

This paper shows how to apply generalized eigenvalue minimization to processes that can be described by a first-order plus time-delay model with uncertain gain, time constant and delay. An algorithm to transform the uncertain first-order plus time delay model into a state-space model with uncertainty polyhedron is firstly described. The accuracy of the transformation is studied using numerical examples. Then, the uncertainty polyhedron is rewritten as a linear-matrix-inequality constraint and generalized eigenvalue minimization is adopted to calculate a feedback control law. Case studies show that even if uncertainties associated with the first-order plus time delay model are significant, a stable feedback control law can be found. The proposed control is tested by comparing with a robust internal model control. It is also tested by applying it to the temperature control of air-handing units.     

Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach

Continuous sedimentation processes in a clarifier-thickener unit can be described by a scalar nonlinear conservation law whose flux density function is discontinuous with respect to the spatial position. In the applications of this model, which include mineral processing and wastewater treatment, the rate and composition of the feed flow cannot be given deterministically. Efficient numerical simulation is required to quantify the effect of uncertainty in these control parameters in terms of the response of the clarifier-thickener system. Thus, the problem at hand is one of uncertainty quantification for nonlinear hyperbolic problems with several random perturbations. The presented hybrid stochastic Galerkin method is devised so as to extend the polynomial chaos approximation by multiresolution discretization in the stochastic space. This approach leads to a deterministic hyperbolic system, which is partially decoupled and therefore suitable for efficient parallelisation. Stochastic adaptivity reduces the computational effort. Several numerical experiments are presented.     

Removing divergence of JCGM documents from the GUM (1993) and repairing other defects

The mission of the Joint Committee for Guides in Metrology (JCGM) is to maintain and promote the use of the Guide to the Expression of Uncertainty in Measurement (GUM) and the International Vocabulary of Metrology (VIM, second edition). The JCGM has produced the third edition of the VIM (referred to as VIM3) and a number of documents; some of which are referred to as supplements to the GUM. We are concerned with the Supplement 1 (GUM-S1) and the document JCGM 104. The signal contribution of the GUM is its operational view of the uncertainty in measurement (as a parameter that characterizes the dispersion of the values that could be attributed to an unknown quantity). The operational view promulgated by the GUM had disconnected the uncertainty in measurement from the unknowable quantities true value and error. The GUM-S1 has diverged from the operational view of the uncertainty in measurement. Either the disparities should be removed or the GUM-S1 should not be referred to as a supplement to the GUM. Also, the GUM-S1 has misinterpreted the Bayesian concept of a statistical parameter and the VIM3 definitions of coverage interval and coverage probability are mathematically defective. We offer practical suggestions for revising the GUM-S1 and the VIM3 to remove their divergence from the GUM and to repair their defects.     

Framework for sensitivity and uncertainty quantification in the flutter assessment of bridges

The phenomenon of aerodynamic instability caused by wind is usually a major design criterion for long-span cable-supported bridges. If the wind speed exceeds the critical flutter speed of the bridge, this constitutes an Ultimate Limit State. The prediction of the flutter boundary therefore requires accurate and robust models. The state-of-the-art theory concerning determination of the flutter stability limit is presented. Usually bridge decks are bluff and therefore the aeroelastic forces under wind action have to be experimentally evaluated in wind tunnels or numerically computed through Computational Fluid Dynamics (CFD) simulations. The self-excited forces are modelled using aerodynamic derivatives obtained through CFD forced vibration simulations on a section model. The two-degree-of-freedom flutter limit is computed by solving the Eigenvalue problem.A probabilistic flutter analysis utilizing a meta-modelling technique is used to evaluate the effect of parameter uncertainty. A bridge section is numerically modelled in the CFD simulations. Here flutter derivatives are considered as random variables. A methodology for carrying out sensitivity analysis of the flutter phenomenon is developed. The sensitivity with respect to the uncertainty of flutter derivatives and structural parameters is considered by taking into account the probability distribution of the flutter limit. A significant influence on the flutter limit is found by including uncertainties of the flutter derivatives due to different interpretations of scatter in the CFD simulations. The results indicate that the proposed probabilistic flutter analysis provides extended information concerning the accuracy in the prediction of flutter limits.The final aim is to set up a method to estimate the flutter limit with probabilistic input parameters. Such a tool could be useful for bridge engineers at early design stages. This study shows the difficulties in this regard which have to be overcome but also highlights some interesting and promising results.     

A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.     

A Bayesian approach to model dispersal for decision support

In agricultural and environmental sciences dispersal models are often used for risk assessment to predict the risk associated with a given configuration and also to test scenarios that are likely to minimise those risks. Like any biological process, dispersal is subject to biological, climatic and environmental variability and its prediction relies on models and parameter values which can only approximate the real processes. In this paper, we present a Bayesian method to model dispersal using spatial configuration and climatic data (distances between emitters and receptors; main wind direction) while accounting for uncertainty, with an application to the prediction of adventitious presence rate of genetically modified maize (GM) in a non-GM field. This method includes the design of candidate models, their calibration, selection and evaluation on an independent dataset. A group of models was identified that is sufficiently robust to be used for prediction purpose. The group of models allows to include local information and it reflects reliably enough the observed variability in the data so that probabilistic model predictions can be performed and used to quantify risk under different scenarios or derive optimal sampling schemes.     

Application of Valuation-Based Systems for the availability assessment of systems under uncertainty

The aim of the paper is twofold. First, it proposes an original application of the Valuation-Based System (VBS) for the availability assessment of systems under uncertainty in a time-varying fashion. Uncertainties related to failure data of components (data uncertainty) and the system structure (model uncertainty) are analysed in the proposed model. Second, it proposes the application of the VBS for the availability assessment of the European Rail Traffic Management System (ERTMS) Level 2 under uncertainty according to the railway dependability standards. The originality of this work lies in the application of the VBS for the availability assessment of systems under data and model uncertainties, and the proposition of a temporal VBS to evaluate the instantaneous system availability.     

A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation

The purpose of this work is the analysis of the uncertainties affecting an electric transmission network with wind power generation and their impact on its reliability. A stochastic model was developed to simulate the operations and the line disconnection and reconnection events of the electric network due to overloads beyond the rated capacity. We represent and propagate the uncertainties related to consumption variability, ambient temperature variability, wind speed variability and wind power generation variability. The model is applied to a case study of literature. Conclusions are drawn on the impact that different sources of variability have on the reliability of the network and on the seamless electric power supply. Finally, the analysis enables identifying possible system states, in terms of power request and supply, that are critical for network vulnerability and may induce a cascade of line disconnections leading to massive network blackout.